Techniques have been widely used that extract an image feature from image data for the detection or identification of an object included in an image (hereinafter, referred to as “object detection”). For example, a technique that uses Local Binary Patterns (hereinafter, referred to as “LBPs”) is described in NPL 1 as one of such object detection techniques (hereinafter, referred to as “conventional technique”).
LBP is a binary pattern created by calculating differences in intensities between each pixel and its surrounding neighborhood pixels and arranging the resulting binary numbers. Gray scale patterns in an image can be extracted using LBPs.
The conventional technique determines LBPs of all or a part of pixels within a region in an image targeted for identification (hereinafter, referred to as “target image”). The conventional technique then generates a histogram of values of the LBPs as an image feature. The conventional technique also generates a classifier in advance using histograms generated from images bounding a predetermined object and images not bounding the object (hereinafter, collectively referred to as “training images”) and stores the classifier. The conventional technique then evaluates the histogram of the target image using the classifier to determine whether the target image includes the predetermined object.
Histograms of LBPs can represent differences in texture and gray scale patterns more accurately than image features such as histograms of oriented gradients (HOGs). Furthermore, the calculation of histograms of LBPs requires less processing cost compared with image features such as HOGs. Thus, the object detection using LBPs, such as the conventional technique, is expected to be applied to various fields.